There are many industries in which the sorting, allocation or otherwise portioning of articles is important, particularly for storage and transport of those articles. This is of particular interest in the food industry, wherein the business of packaging, storing and transporting food from its source to the retailer or end user is very important. It is highly desirable to portion food items in the most efficient possible manner in order to protect and preserve food items whilst at the same time making the optimum use of the space available, to improve the cost effectiveness of the process.
Many known approaches use algorithms in an attempt to optimize the number and nature of items to be placed in one or more containers having particular capacity and target fill levels. A particular problem for filling containers with a predefined target item weight, using a limited number of items of known weight and having a limit on the number of containers that can be opened for filling at any given time, is known as the “on-line bounded-space bin-cover problem”. The problem of finding an optimal bin cover is known to be NP-hard, making it unlikely that an efficient polynomial algorithm exists for finding an optimal solution to the problem.
In the food industry there are several grading and portioning tasks which are primarily done by hand since known algorithms are not suited to address or automate these tasks. For example, fresh fish fillets and poultry breast fillets are commonly packed in so-called interleaved packs, which is one example of a packing method which puts extra burden on algorithm portioning solutions. Interleaved packs are typically block frozen. The fillets are placed in layers where the fillets are not touching and then there is a plastic film placed on top of the layer before the next layer of fillets is placed in the pack. These packs are commonly selected by restaurants as they are very compact but the fillets are not frozen together so any number of pieces can be taken from the pack for cooking.
When packing cod fillets in interleaved packs it is most common to have three fillets in each layer and the fillets in the first layer are placed as shown in FIG. 1 where the tails are placed towards the centre of the pack while the thicker loin parts are towards the edges. The Figure shows as well that two fillets have the tail part pointing downwards and one upwards. When the next layer is placed this is opposite as two fillets have now the tail part facing upwards and one downwards but the tails are still placed towards the centre. In addition to these requirements the packs must be of fixed weight and can also have a requirement that a certain predefined number of fillets must be in the pack.
To be able to make an interleaved pack fully automatically there are two known alternatives. One is to make a more complicated grader machine which can selectively rotate the fillets and then the associated batching algorithm does not have to take into account the orientation of the fillets. The other method is to place every other fillet in the packing machine with tail facing to the left while the other fillets have the tail facing in the opposite direction. This will however put a requirement on the portioning algorithm used, to be able to select fillets. Current methods which are purely based on combinatory or statistical methods are not at all suited for such a task.
Both for interleaved and other packing arrangements, in the food industry there is a high demand for grading and packing articles into packs of fixed weight. In the last years, some progress has been made in the development of methods for grading and packing articles into packs of fixed weight. These methods are sometimes referred to as “intelligent batching” wherein the articles are intelligently selected into different batches (portions) to make them as close as possible to a predefined target weight. When aiming at creating so called fixed weight portions—that is, portions which have a weight as little as possible above a predefined minimum weight—there are mainly two methods which are used; accumulation weighing and combination weighing.
According to the accumulation weighing principle, items that are to be allocated to a container or other packaging or storage means are weighed on a dynamic scale, and the weights are registered in order to keep track of the relevant placement of the items in a line and of the corresponding weights. A distribution unit then allocates the individual items to one or more receiver bins, until the accumulated weight in a particular bin matches a target weight. However, accumulation weighing has several disadvantages. Typically in these methods the weight of only a single article is known when a decision is made as to which of a plurality of receiver bins the article will be guided to. This creates significant disadvantage as the method can not ensure that a portion in a receiver bin will be finished a certain weight above the target weight as it can only predict with certain probability that the method will be able to complete the portion within a given weight. It is furthermore hardly feasible to use this method to create packs with special requirements like in the case with interleaved packs.
U.S. Pat. No. 5,998,740 discloses a weighing and portioning technique based on a “grader” technique, which is a development of accumulation weighing, wherein a number of articles which are to be portioned out, namely natural foodstuff items with non-uniform varying weight, are fed through a weighing station and thereafter selectively fed to a plurality of receiver bins. The technique includes weighing a finite number of items which are to be portioned out and using the distribution of weights of those items to statistically evaluate the best possible apportionment of the articles.
Another known method is described in patent EP01218244B1 in which, instead of the prospect functions for the filling of one or more bins being generated based on a single item, the prospects are based on a so-called Tally Intelligent Batching Algorithm. The tally is the total number of possible combinations of items present in a first-in-first-out (FIFO) queue as a function of batch shortage and item count. This method can utilize knowledge of having more than a single item to be allocated of known weight but as it works with the number of possible ways of creating combinations as opposed to trying out actual assignments of the items to the bins it may overlook good assignments. Furthermore, it is computationally intensive and inefficient and is not suited to solve special packing requirements for example when packing interleaved packs.
According to the combination weighing principle, the combined weight of multiple articles to be allocated are known. Typically there are multiple weighing hoppers and the articles from any of the hoppers can be released to create the portions to fill a container or bin. There is thus random access to the weighing hoppers and hoppers which have been emptied can be selectively filled. By using combinatory algorithms, the combination of hopers which generates the least give-away is selected and subsequently the articles are released from these hoppers and unified to create the portion. The weighing hopper machines which utilize this combination weighing method are typically either in circular arrangement or in a linear arrangement as described in U.S. Pat. Nos. 4,442,910 and 4,821,820. The main disadvantage of this method is that random access to many hoppers is expensive to build and also difficult to implement when handling delicate items such as fresh fish products, for which it is preferred that the articles go directly after weighing into the final packs rather than to be placed in intermediate weighing hoppers and collected in the pack with a shoot or intermediate conveyor as is typically done in multi-head weighers.
Yet another set of methods exists wherein the weight of multiple articles are known, as in combination weighing, but the pieces are never the less fed sequentially into one or more receiving bins or containers. One example of such portioning method is in patent EP01060033B1. This method uses portion accumulation stations to temporarily hold one or more items until their weight is suitable to be added to a bin, to help achieve its target fill level. The main drawback of this method is that does not perform grading at the same time as portioning is done. Furthermore, if few number of articles with known weight are currently in the machine, it can not perform the portioning task accurately. Moreover, it requires additional space and apparatus for the accumulation stations, which adds to the overall expense.
The invention is set out in the claims.
Because a method of allocating an item within a group of items to a selected receiver from a group of receivers is provided, wherein a characteristic property of both that item and a selected other item within that group is considered along with capacity information on the receivers, an optimal allocation can be achieved. The allocation doesn't rely on assumed or predicted information, nor on statistical trends, but on actual characteristics of the item to be allocated, and of an item that may be allocated subsequently. The characteristic property of the items can include any of size, weight, length and orientation, or any other suitable property dependent on the type of item being considered. Because the method considers possible options for allocating both the first item and the second item to the group of available receivers, the knock-on effects of possible allocation choices for the first item can be looked at, and in particular the allocation of the first item can be selected so that viable options remain for allocation of the second item thereafter. Therefore the optimisation is not just instantaneously beneficial and accurate but has future considerations in mind also.
Because the successive addition of a plurality of items to the group of receivers can be considered, a cumulative picture of the effects of each allocation option is provided. This increases the intelligence of the method, and helps to refine and further optimise the item allocation process. Furthermore, by considering successive allocation of the plurality of items to two or more different combinations of receivers and comparing the respective effects of those allocation options on the capacity information for the group of receivers, the actual real world effects of the available allocation options are considered and the most favourable allocation option can thus be selected for determining which receiver the first item should be allocated to. Hence a more accurate solution is provided, which can deal with real world occurrences such as anomalies in the general trend of characteristic property information for items in the group of items.
By enabling the actual capacity of a receiver after allocation of an item thereto and/or a predicted capacity of a receiver after allocation of an item thereto to be considered, the look ahead abilities of the method are enhanced. That is, the method can use the actual characteristic property information it has regarding the items in the groups to extrapolate and predict future capacity effects which may influence the choice of receiver for allocation of the first item thereto. Because a predicted capacity of a receiver after allocation of an item thereto is based not only on historical allocation information but also on current allocation information, including characteristic property information for the actual remaining items to be allocated and/or capacity information for the actual receivers available for allocation of the those items thereto, the prediction can be better refined as compared to prior art methods that rely solely on previous trends or distributions. That is, any anomalies or unusual features of the actual items and receivers being considered can be taken into account when calculating how the capacity of a particular receiver might change after allocation of an item thereto.
By considering both a current fill level and a target fill level for the receivers, the allocation of the item can be better focused—allowing it to be allocated to the receiver in which it would be most useful in enabling the target fill level to be achieved efficiently, and avoiding or at least reducing the waste and potential loss of profits associated with an overfilled receiver. By looking ahead to see what the resulting fill level would be if the item was added to a particular receiver within the available group of receivers, a clear picture of how best to allocate that item or another item in the group can be obtained.
In particular, because the resulting receiver fill level can be considered if the other, second item was added to any of a number of different receivers within the available group of receivers, it is possible to look and plan ahead as part of the allocation process. That is, an allocation that would work well for the first item might prove to be less than optimal if the second item is considered.
By going one step further and looking at the resulting fill level if each of the items in the group of items available for allocation were allocated to one or more available receivers, the future planning and optimisation benefits are enhanced. This benefit is derived not just for allocation of the current group of items but can also be used in planning and controlling sorting and provision of subsequent groups to items for allocation. However, even when possible allocation of each of the items in a group of items is considered, only the allocation of the first of those items will be determined as a result. Ideally, updated item characteristic property information and capacity information is used every time a new item within the group of items is to be determinatively allocated.
By limiting the time period during which allocation determination is carried out, a balance is achieved between looking ahead to improve the optimisation of allocation of an individual item, and keeping the allocation process moving at an acceptable rate.
By discontinuing consideration of addition of an item to a particular receiver or combination of receivers, if that allocation option appears to provide a less favourable result than a result which an alternative option has already been shown to provide, computational efficiency and speed of the allocation process are improved. Furthermore, prioritising a particular receiver or combination of receivers based on capacity information allows an optimal allocation solution to be reached more quickly and efficiently. Similarly, ignoring a receiver or combination of receivers according to capacity information prevents wasting time on “dead-end” options, thus improving overall efficiency and speed.
By enabling user-defined constraints to be considered, flexibility and additional control is provided. Dependant on the particular items to be allocated and other real world considerations, the user defined constraints can include any number of factors including the number of items in the group of items, the number of receivers in the group of receivers, the number of items to be allocated per receiver, and any time limit for filling a receiver to a target level before it is replaced by a new, empty receiver. Furthermore, the orientation of an item for allocation and its configuration with one or more other items once allocated to a receiver can be considered. Therefore, particular packing arrangements such as interleaving can be efficiently and usefully incorporated into the allocation process.
Once the allocation determination has been made, the first item can be directed to the selected receiver.
By predicting future capacity information for the group of receivers using the allocation determined for the first item and a characteristic property of at least one of the remaining items in the group of items, the method enables intelligent selection of the next items for allocation and assists in the allocation determination for that subsequent item. In particular, because future receiver fill level can be predicted, allocation of the current group of items can be tailored accordingly and/or the provision of a future group of items for allocation of receivers for receiving such items can be intelligently selected.
By ensuring that the characteristic property information and capacity information is updated for the subsequent item to be allocated, it is ensured that at all times the most relevant and accurate available information is made use of, rather than relying on previously obtained or assumed trends or patterns which might have applied to earlier items but which cannot be guaranteed to apply equally well to the subsequent items to be allocated.
Therefore a method and associated control and operation is provided that has substantial advantages over prior art methods. The approach is accurate, efficient, intelligent and flexible, whilst at the same time being straight forward to implement using existing conveyance and receiver apparatus, and for a variety of item types.